# Monte Carlo Tree Search

Graph neural networks learn structure–property relationships from graphs of atoms and bonds, but those relationships stay in a latent space and are hard to read as scientific rules. Graph symbolic distillation (GSD) first uses a constrained message-passing architecture search to fix how information is transformed, propagated and aggregated, then fits each layer's learned transformation with a sparse symbolic expression while keeping graph connectivity and aggregation. Across halide-perovskite band gaps, molecular hydration free energies and multidentate adsorption on transition-metal surfaces, it rebuilds expert descriptors from elementary node and edge features and yields testable local-geometry, polarity–size and electronic–connectivity relationships.

Graph neural networks learn structure–property relationships from graphs of atoms and bonds, but those relationships stay in a latent space and are hard to read as scientific rules. Graph symbolic distillation (GSD) first uses a constrained message-passing architecture search to fix how information is transformed, propagated and aggregated, then fits each layer's learned transformation with a sparse symbolic expression while keeping graph connectivity and aggregation. Across halide-perovskite band gaps, molecular hydration free energies and multidentate adsorption on transition-metal surfaces, it rebuilds expert descriptors from elementary node and edge features and yields testable local-geometry, polarity–size and electronic–connectivity relationships.

LLM-driven interactive narrative systems have been hard to compare because each experience manager is tied to its own bespoke scenario. An LLM-based narrative platform turns experience managers into interchangeable modules over a shared symbolic world model of facts, operations and goals, and every applied operation carries explicit preconditions and effects, so any session can be replayed as a common state-transition path. On a Little Red Riding Hood scenario, open generation, Monte Carlo repair and autonomous characters each ran 100 simulated sessions, and no manager led on all four properties: player agency, manager controllability, author intent and robustness.

LLM-driven interactive narrative systems have been hard to compare because each experience manager is tied to its own bespoke scenario. An LLM-based narrative platform turns experience managers into interchangeable modules over a shared symbolic world model of facts, operations and goals, and every applied operation carries explicit preconditions and effects, so any session can be replayed as a common state-transition path. On a Little Red Riding Hood scenario, open generation, Monte Carlo repair and autonomous characters each ran 100 simulated sessions, and no manager led on all four properties: player agency, manager controllability, author intent and robustness.

Retrosynthesis prediction shifts from emitting reactants directly to a four-stage reasoning trace that mirrors a chemist's disconnection strategy: product analysis, key-substructure identification, strategic bond disconnection, and synthetic-equivalent mapping. The model is fine-tuned on structured rationales from SyntheticRetro, then trained with reinforcement learning on a round-trip reward that passes predicted reactants through a forward synthesis model and rewards reconstructions of the original product. On the 500-instance ORDerly main test set, RetroReasoner (RL) reaches Exact@1 of 0.526 and Round-trip@1 of 0.826; on the rare atom/token subset it reaches a feasible ratio of 0.557 and template diversity of 2.97, above the same-architecture Prediction-Only (RL) at 0.478 and 1.97.

Retrosynthesis prediction shifts from emitting reactants directly to a four-stage reasoning trace that mirrors a chemist's disconnection strategy: product analysis, key-substructure identification, strategic bond disconnection, and synthetic-equivalent mapping. The model is fine-tuned on structured rationales from SyntheticRetro, then trained with reinforcement learning on a round-trip reward that passes predicted reactants through a forward synthesis model and rewards reconstructions of the original product. On the 500-instance ORDerly main test set, RetroReasoner (RL) reaches Exact@1 of 0.526 and Round-trip@1 of 0.826; on the rare atom/token subset it reaches a feasible ratio of 0.557 and template diversity of 2.97, above the same-architecture Prediction-Only (RL) at 0.478 and 1.97.